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willingness to
adhere to trial and
treatment proto-
cols. Because trial
protocol eligibility
produces the subset
of the target popu-
lation who is will-
ing and able to
participate in a
clinical trial, gener-
alizability is com-
promised. In
effectiveness trials
where the premium
is on external valid-
ity, these criteria
are often relaxed.
Because trial pro-
tocol eligibility is relaxed in these studies, which tend to take “all comers” regard-
less of their ability to complete the trial protocol, internal validity is compromised.
The importance of these respective goals, but the difculty in achieving all of them
in the same trial, is the basis for progressive translational research.
Patient-centered eligibility focuses on the criteria used to identify the target popula-
tion. Most eligibility criteria for a behavioral clinical trial exclude patients for medical
or logistical contraindications. But they vary in their approach to behavioral eligibility.
Patient-centered eligibility is sensitive to patient preferences. These preferences can
be assessed indirectly using run-in periods or directly using specic questions.
Run-in periods make behavioral eligibility contingent on performance of actual
behaviors essential to trial evaluation. They tend to focus on such trial protocol
requirements as self-monitoring, completion of selected assessments, and/or
attendance at scheduled visits. But they can also be an indirect measure of moti-
vation. The more extensive the run-in, the more motivated a prospective partici-
pant must be to complete it successfully. Eligibility based upon successful
completion of a run-in has been shown to enhance adherence to treatment and
reduce attrition from a trial [47–49].
It is more difcult to make eligibility contingent upon answers to questions that
assess motivation or intention. Indeed, motivation may be an intrapersonal charac-
teristic that may or may not be known even to the prospective participant. Motivation
may be best judged by an informed prospective participant. A specic list of all of
the actions and behaviors that are required by a treatment may help the prospective
participant better understand the time commitment involved and whether it can be
accomodated given the demands already faced in his/her life. Single questions pre-
dict treatment success and can be used to initiate discussions about t. For example,
Two types of eligibility criteria
in an efcacy trial
ELIGIBILITY
CRITERION
TARGET
POPULATION
TRIAL
PROTOCOL
MEDICAL Able to benet
medically
Able to complete
follow-up
DEMOGRAPHIC Representative
of the population
at risk
Absence of barriers
to protocol
implementation
BEHAVIORAL Motivated to
undergo
behavioral
treatment
Motivated to
adhere to treatment
and trial protocols
LOGISTICAL Able to receive
intervention
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a pre- contemplator will state that they have no interest in making any changes now
or in the near future. A person who is destined for success at weight loss may be
identied by a single question about his/her degree of condence in sustaining
physical activity [50, 51].
Patient-centered eligibility aims to produce a target population that not only needs,
but also wants, a behavioral intervention. This accurately reects those to whom the
results of a behavioral trial can be generalized. A Phase II or Phase III behavioral
efcacy trial is conrmatory. It is a place to evaluate how well a treatment works in
those who are well aligned with it. It is not a place to enhance motivation to try it.
This latter goal is important but better placed in early treatment development activi-
ties, such as those proposed by the ORBIT model [52]. A target population that
needs and wants a behavioral treatment will maximize the internal validity of the
trial by minimizing differential dropout, minimizing confounding, and reducing the
need for per-protocol analyses. It is also likely that it will maximize the overall suc-
cess rate for behavioral trials.
Patient-Centered Recruitment
Prospective participants for a clinical trial make decisions based upon whether they
need and want a particular treatment and whether they want to participate in a trial.
This calculus involves a variety of considerations including, for example, strength
of preference for a treatment, likelihood of getting it in the trial, burden imposed by
trial participation, and the life context within which participation is set. Patient-
centered recruitment encourages prospective participants to consider all of these
criteria carefully when making a decision about whether or not to participate in a
behavioral trial.
Successful recruitment in a clinical trial is characterized by meeting recruitment
goals on time, maximizing the diversity of participants who are at risk for the dis-
ease, and recruiting those who are well aligned with the treatment. There is a large
literature on overcoming barriers to recruitment. Table8.4 provides a selective sum-
mary, identifying four key barriers: access, burden, distrust, and ambivalence. The
challenge of reaching the target population tends to be overcome by using multiple
methods, including internet and social media approaches and the innovative use of
approaches that have been successful in the marketing literature. The challenge of
the burden of participation can be overcome by making use of the internet and elec-
tronic medical records to simplify recruitment and treatment and by reducing exten-
sive assessment batteries. The challenge of distrust is most effectively overcome by
drawing on community-based participatory approaches which create partnerships
with trusted community leaders, organizations, and individuals.
But the “elephant in the room” that receives far less attention is how to recruit the
prospective participant who is ambivalent (i.e., has mixed feelings about participat-
ing) or reluctant (i.e., hesitant to participate). The most common approach is the
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“hard sell,” fostered by the reality, in most trials, of recruitment that lags behind
milestones and the pressure put on recruiters to recruit more and faster. Under this
pressure, reluctance and ambivalence tends to be converted by taking a “pro-
enrollment” position which reinforces the pros for participating and problem-solves
around the cons. Unfortunately, the “hard sell” approach may result in a Pyrrhic
victory characterized by a short-term gain followed by a long-term loss. A partici-
pant may agree to enroll, only to vote with his/her feet later by not adhering to the
treatment and weakening its effect size, and/or by dropping out and compromising
the randomization. This is particularly problematic for a behavioral trial where
something about the arm to which one is randomized encourages differential
dropout.
An alternative to this “hard sell” approach to converting ambivalence is the innova-
tive method provided by Goldberg and Kiernan [71]. Through the vehicle of a
group- based orientation session held several weeks prior to randomization, a
prospective participant is provided with the opportunity to explore his/her ambiva-
lence by helping him/her understand what is involved in participation, what is
Table 8.4 Recruitment challenges and ways to overcome them
Barrier Challenge Approach
Access [53–59] Patient-centered
information does not reach
the target population
•
Recruitment materials:
– Professionally designed
– Use marketing principles
•
Multiple recruitment methods
•
Personalized telephone follow-up
Burden [
53,
59–64]
Enrollment and intervention
are time and resource
intensive
•
Recruit using internet, phone, social media,
mobile devices, electronic medical records,
registries
•
Intervene with minimal inconvenience using
technology, exible schedules, convenient
locations, compensation for time and travel,
and limited assessment batteries
Distrust
[
59, 65–69]
Special populations distrust
research and researchers
• Community-based participatory approaches
– Community advisory board
– Community health workers
– Community recruitment and treatment sites
(e.g., barber shops, churches)
Ambivalence
[
17, 70, 71]
Recruitment demands
reduce time needed to
convert reluctance into
participation
•
Realistic recruitment plans based upon prior
recruitment feasibility study
• Run-in periods before randomization to assess
adherence
• Sequential recruitment process providing
opportunity to consider pros and cons of
participation
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required by the science of the trial, and how realistic it is for the prospective partici-
pant to meet the requirements of the trial. During the orientation, staff use motiva-
tional interviewing techniques to provide the prospective participant with the
opportunity to articulate explicitly the pros and cons of being randomized to the
treatment arm and, equally important, the comparator arm. The group makes it pos-
sible for potential participants to listen to each other’s pros and cons, thus eshing
out a full range of reasons. Prospective participants are then explicitly asked to
reect on the pros and cons over the upcoming week before deciding whether they
want to continue by moving to baseline assessment. In a descriptive study of this
approach in a behavioral weight management trial [71], 71% of the 228 who attended
the orientation session went on to be randomized into the trial. Of the 162 who were
randomized, 159 (98%) completed the 6-month follow-up, and 156 (96%) com-
pleted the nal 18-month follow-up. There was no differential dropout by trial arm.
By giving prospective participants the time to evaluate the pros and cons of partici-
pation, there will be a certain percentage who will opt out. In the Goldberg and
Kiernan study above, this was 29% of those who expressed initial interest. When
given the time to make this decision without pressure from the investigators, they
decided that they were not well aligned with the trial. Generalizability of results
from this trial will be to those who need and want the treatments under consider-
ation. Acceptance of a decision not to participate in some who express initial inter-
est is a way to show respect for potential participants. Moreover, those who decide
that they are well aligned with the trial will be less likely to drop out or fail to adhere
to the protocols, maximizing a conclusive result. More empirical studies are needed
in this important area.
Patient-Centered Consent
Informed consent procedures were instituted to protect participants from getting
involved in studies in which they are unaware of the risks [72]. But often informed
consent procedures are cryptic, excessively long, and confusing. They may cover all
of the legal bases, but the price is in prospective participant comprehension. Patient-
centeredness maximizes the ability of prospective participants to understand fully
what they are getting themselves into before, rather than after, they are randomized.
An excellent example of how to do this comes from the literature on placebo effects
[73]. A clinical trial evaluated the value of arthroscopic surgery for knee osteoarthri-
tis. Participants were randomized to one of three arms. Two arms were for real sur-
geries, but the third arm was for a sham surgery that simulated the actual activities
involved in the real surgeries. Under the guidance of Baruch Brody, the clinical trial
ethicist, prospective participants underwent standard informed consent procedures.
But in addition, they were asked to write, in their own hand, the following.
“On entering this study, I realize I may receive only placebo surgery. I further realize that
this means that I will not have surgery on my knee joint. This placebo surgery will not
benet my knee arthritis.” [73]
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The results of this trial both at the 1-year and the 2-year follow-ups were that all
three arms showed benet on a subjective measure of pain and an objective measure
of physical function. There was no difference in benet across the three arms. The
sham surgery was as effective as the real surgeries.
This informed consent procedure resulted in 44% of the eligible prospective partici-
pants declining to participate. This high rate of nonparticipation suggests that par-
ticipants completely understood that they had a one-third chance of getting sham
surgery without any known benet. In the calculus of their decision-making, these
patients may have decided that a 33% chance of getting a sham treatment was not
worth the demand of the trial protocol to undergo a surgery that simulated in every
detail what would have been required by a real surgery.
Of the 56% who did participate, there was greater than 90% completion of the
2-year follow-up assessments in all three arms. The patient-centered informed
consent protected the random assignment and thus the internal validity of the trial.
But there was no benet of surgery over sham. In the calculus of their decision-
making, the participants may have been more willing to take the 66% chance of
getting the real surgery relative to the nonparticipants. But if these “risk takers” also
happened to be more susceptible to the placebo effect (i.e., an unknown confounder),
a selection bias resulting from the rigor of the control group could have attenuated
detection of a true benet from the arthroscopic surgery. If the goal of the trial was
to disentangle non-specic from active treatment components, the rigorous sham
control was the optimum choice. But if the goal was to evaluate the benet of the
surgery on outcomes, selection bias could have underestimated the true benet if
those most likely to respond to surgery, but not placebo, did not participate. A com-
parator that was linked closely to this goal of the trial, such as current drug treat-
ment for knee pain, may have been a better choice. (See Chapter 6: The Choice of
a Comparator.)
Other techniques that have been used to maximize prospective participant under-
standing of what is involved in a trial include the use of participant-centered video-
tapes explaining the trial [74], short, simple descriptions (e.g., A placebo pill is an
inert or inactive pill, like a sugar pill, without any medication in it.) [75], and a
request to have the prospective participant reiterate out loud, or in their own hand-
writing, what is involved in the trial from his/her perspective [73]. The point of all
of these techniques is to insure that prospective participants fully understand the
nature of the trial and what they are expected to do, thereby maximizing the chance
that once they are randomized they will stick with it and random assignment will be
protected.
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Patient-Centered Retention
There is some imprecision in the way retention issues are described. Retention can
refer to adherence/compliance to a treatment protocol or to a trial protocol. It can be
phrased in the positive (i.e., adherence, compliance) or in the negative (i.e., dropout,
withdrawal, non-adherence). For purposes of this discussion, retention, attrition,
and dropouts refer to the trial protocol, while adherence and compliance refer to the
treatment protocols. Low retention jeopardizes the random assignment and opens
the door to confounding. Non-adherence to the treatments weakens the efcacy of
the intervention and thus reduces the effect size of the trial.
Recruitment and retention are linked. The broader the eligibility criteria for a trial,
the greater the risk of poor retention. The push to recruit can result in successfully
meeting recruitment goals, but if it results in poorer than expected retention, the
success is short-lived. The goal is to nd ways to achieve recruitment success while
also retaining those recruited. Once a participant is enrolled, retention is essential
for protecting randomization.
A number of strategies have been identied to enhance retention (Table 8.5).
Although all are important, perhaps the most important is the participant experi-
ence. In most behavioral or prevention-oriented trials where the discomfort from a
disease is minimal, adherence to a trial protocol may become relatively unimportant
when faced with more immediate problems that compete for attention such as
schedule conicts, family demands, transportation problems, lack of time, and too
Table 8.5 Common retention strategies
Focus of Strategy Description
Design of trial [76–78] – Minimize time commitment for intervention and assessments
– Avoid uncomfortable, invasive tests
Recruitment [38, 76, 79] – Obtain permission to contact family
– Involve family in consent
– Screen out those unable to adhere to protocol
– Include a run-in to assess motivation, commitment, availability
Follow-up [38, 62, 76,
77, 79]
– Increasing nancial rewards for each follow-up visit
– Provide transportation or reimburse for its cost
– Maintain regular contact with participant: mobile phone,
newsletters, trial results
– Maintain continuous community contact: trial procedures, trial
results, risks vs benets
Participant Experience
[38, 80]
– Show gratitude
– Provide support
– Provide opportunities for meaningful social activity
– Facilitate positive experiences
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much stress [81]. Participant perception of the trial being associated with warm,
supportive people, enjoyable activities, and pleasurable emotions may be the most
powerful way to enhance retention. Budgeting time and resources for these activi-
ties proves to be well worth the effort.
Secondary “Per-Protocol” Analyses
Protection of random assignment translates into an analytic strategy called intent-
to-treat in which all randomized participants are included regardless of their level of
participation. This can present a “perfect storm” for the behavioral trialist caused by
three interrelated “squalls.” (1) The demand for participant volition combined with
long follow-ups raises the risk of non-adherence and dropouts. (2) Analyses of
results using all who were initially randomized, regardless of the dose of treatment
received, weaken the efcacy of a treatment and undercut power to detect a benet.
(3) Some questions of interest require a different analytic strategy, such as How
much benet is there for those able to adhere to the treatment? This “perfect storm”
can be resolved by establishing primary and secondary analyses. Priority in any
clinical trial, regardless of the context, is to get a conclusive result on the primary
outcome. This is accomplished by minimizing the possibility of confounding
through the protection of random assignment and analyzing results as “intent-to-
treat.” But this may be a Faustian bargain. It may be equivalent to selling one’s soul
by sacricing a question of interest in pursuit of methodological purity. Clinicians
and policy-makers may need to make decisions based upon evidence about whether
or not a treatment works in those who choose to adhere to it.
Secondary analyses can explore “per-protocol” effects of treatment in those who
adhered to it. Because such analyses break the balance achieved by randomization,
they are subject to confounding which requires appropriate covariate adjustment
and clear interpretations that acknowledge the problems of unmeasured and
unknown confounding. Alternatively, incorporation of a measure of adherence in
the assessment protocol, and testing adherence by treatment interactions in multi-
variate models, can help to estimate the magnitude of the effect of adherence, as
well as the differential effect of adherence by trial arm, on the outcome [81]. These
secondary analyses, however, are hypothesis generating. A new test of this treat-
ment, after it has been rened to improve adherence, would be needed using intent-
to-treat analyses as the primary analytic strategy.
The combination of a primary “intent-to-treat” analysis with a secondary “per-
protocol” analysis provides a thorough understanding of trial results [82]. But even
this approach is not without its limitations [83]. However, the designation of pri-
mary and secondary analyses resolves the temptation to forego the methodologic
beauty of the randomized design in pursuit of a question that is important but more
difcult to answer.
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SELECTED EXPERIENCE
FROMBEHAVIORAL TRIALS
Behavioral Eligibility inaSuccessful Behavioral Clinical Trial
The Diabetes Prevention Program (DPP) was among the greatest successes ever
achieved by a behavioral clinical trial [84]. In this trial of insulin-resistant patients,
a lifestyle intervention reduced the incidence of diabetes by 58% compared to pla-
cebo pill and by 31% compared to metformin, the drug of choice.
What is often not recognized, however, is that the population under study was highly
selected [85, 86]. Of the 158,000 people screened at 27 clinical centers, 30,986 were
deemed to qualify based upon objective evidence of being insulin resistant. Of
these, 3234 were randomized into one of three arms. Eligible participants were
nondiabetic but with a high risk of progressing to Type II diabetes during the trial
follow-up period. Ineligible participants had conditions which increased risk of
adverse events from the interventions, shortened life expectancy, interfered with the
conduct of the trial, or affected the assessment of Type II diabetes. The trial popula-
tion turned out to be 2.1% of those screened and 10.4% of those who were screened
and found to be insulin resistant.
Eligibility also included behavioral eligibility. To get into the trial, prospective par-
ticipants had to successfully complete four screening steps that took place over
4–13weeks, and were ordered by progressive levels of difculty and expense. At
Step 1, a simple fasting or casual glucose measurement was undertaken. At Step 2,
an oral glucose tolerance test was administered after a 12–14-hour fast in which no
smoking, exercise, or any unusual activity was permitted. At Step 3, a 3-week run-in
was undertaken to assess adherence to placebo pills and willingness to undergo
detailed record-keeping of diet and physical activity. At Step 4, a pregnancy test for
women and a nal checklist for eligibility were used to determine eligibility for
randomization.
It is likely that the 10.4% of insulin-resistant participants in this trial were a highly
selected and highly motivated subgroup of all participants at risk. They underwent
progressive tests over 4–13weeks giving them ample time to consider both the pros
and cons of participating in both the trial and in the treatment, if they were random-
ized to receive it. At the stage where a causal inference about the efcacy of a treat-
ment is needed, the DPP investigators protected the random assignment and thus the
internal validity of the trial by selecting motivated participants whose alignment
with the treatment was demonstrated by their success with a prolonged run-in.
Despite this selectivity, the DPP has had far-reaching implications and is a testa-
ment to progressive translational research. It opened the door to a large number of
effectiveness studies that evaluated the DPP program in such community settings as
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the YMCA, churches, and medical clinics for the underserved. It formed the basis
for success at achieving third-party reimbursement for the costs of undergoing this
program.
Some have argued that the “trickle down” model of translating efcacy trial ndings
into practice is awed, and use this as a justication for broad eligibility criteria
which result in great diversity in participants [37]. But the DPP has shown the aw
in this argument. This efcacy trial did indeed “trickle down” to effectiveness stud-
ies and clinical practice. This is an excellent example of the spirit of progressive
translational research where the best candidates for evaluation in effectiveness stud-
ies are those interventions that have been shown to be efcacious.
Failure toProtect Random Assignment
In the early 1980s, there was mounting evidence that periods of emotional stress
precede acute ischemic heart disease episodes. Therefore, Frasure-Smith and Prince
[87] reasoned that monitoring stress in post-heart attack patients and, when stress
was high, sending a nurse to the home to intervene as needed could be a simple and
cost-effective approach to the prevention of recurrent events. They randomized 769
post-heart attack men to treatment or standard care, conducted a 1-year nurse inter-
vention, and followed participants for an average of 5 years to examine mortality
and cardiovascular recurrences [87, 88].
Figure 8.4 presents the
design of the trial. The
IRB required informed
consent to be obtained
after participants were
randomized to a trial arm
to make it possible for
prospective participants to
have an accurate under-
standing of what was
required for participation.
Thus, participants pro-
vided consent to partici-
pate in the arm to which
they were randomized.
This approach resulted in
a large dropout rate and,
perhaps more importantly,
a differential dropout rate.
Eligible Subjects
769 Post-MI Men
(Randomization)
=
=
Treatment
N=397 N=372
After dropouts After dropouts
N=232 (58%) N=229 (62%)
Distress
Distress
CHD
CHD
N=176 (44%) N=179 (48%)
Control
Fig. 8.4 Dropouts in the Ischemic Heart Disease Life Stress
Monitoring Program [88]
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Approximately 40% of the randomized participants dropped out before the treat-
ment began, thus compromising the balance achieved by the initial randomization.
But different types of people dropped out of each of the treatment arms. In the
treated arm, more participants of lower socioeconomic status dropped out, possibly
because of the requirement that nurses come to the home. In the control arm, more
participants of higher socioeconomic status dropped out, possibly because they saw
participation as a waste of time.
Differential dropout
opened the door to con-
founding between treat-
ment arm and
socioeconomic status.
Participants in the treated
arm were of higher socio-
economic status, and par-
ticipants in the control
arm were of lower socio-
economic status (Fig.8.5). During the course of the 5-year follow-up, more partici-
pants were lost from each arm, ending up with only 44% and 48% of the original
randomized participants in the treated and control arms, respectively.
The results of the trial on
mortality at the end of the
program year are por-
trayed in the two bars at
the left in Fig.8.6. There
were almost twice as
many cardiac deaths in the
controls as there were in
the treated (p=0.07) [87].
But the large and differen-
tial dropout rates provided
an alternative explanation:
men of higher socioeco-
nomic status had lower
mortality than men of
lower socioeconomic sta-
tus [89]. Because of the
confounding between
treatment arm and socio-
economic status, and the
inability to control for this
confounder because of missing data, the trial was inconclusive.
Higher
Socioeconomic
Status
Lower
Socioeconomic
Status
Treated
Controls
Fig. 8.5 Confound between treatment arm and socioeconomic
status in the Ischemic Heart Disease Life Stress Monitoring
Program [88]
Fig. 8.6
Comparison of cardiac deaths in men in the
Ischemic Heart Disease Life Stress Monitoring Program
[88] and the M-HART trial [90]
10
8
6
4
2
0
Treatment TreatmentControl Control
Ischemic Heart Disease Life
Stress Monitoring Program
M-HART
[higher SES] [lower SES]
Cardiac Deaths (%)
5.2%
9.6%
P = 0.07
(ns)
2.4%
2.5%
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